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This is Part 2 of my series on the 7-Layer Agentic AI Framework. Part 1 covered the Perception Layer, how agents ingest and normalize raw inputs. The Semantic Layer picks up right where perception leaves off: perception handles what the agent sees; the semantic layer governs what the agent knows and what it’s allowed to […]

Co-authored by Stew Chisam, Operating Partner, StellarIQ If you blinked recently, you might have missed a major shift in the AI landscape. In the span of just a few weeks, the industry has delivered a rapid succession of sophisticated releases: OpenAI’s GPT 5.2, Anthropic’s coding specialist Claude Opus 4.5, and Google’s efficiency engine, Gemini 3 Flash.

Why Karpathy’s “Decade of the Agent” Means You Need to Start Today Andrej Karpathy just handed executives everywhere what might sound like permission to wait. In a recent Dwarkesh Patel podcast, the AI expert said 2025 isn’t the “year of the agent,” it’s the “decade of the agent.” I can already hear the boardroom conversations:

You must take a human-centric approach to engage a skeptical workforce. Picture this: You’re a leader introducing new AI tools that promise to revolutionize how work gets done. The executives are thrilled. The consultants are celebrating. But when you look around your team, you see crossed arms, furrowed brows, and a Slack channel full of

AI is at an inflection point. Models are powerful, tools are plentiful, and pilots are easy to launch. Yet most organizations stall when moving from prototypes to meaningful transformation. The constraint isn’t intelligence; it’s execution. The solution is not new. For decades, the best engineering outcomes have come when builders sit close to users. Palantir

ST. LOUIS, MO (Sept. 16, 2025) – In construction, an accurate bid can mean the difference between a profitable year or continuous delays and costly overruns. Many contractors continue to rely on spreadsheets, fragmented data, or gut instinct to prepare bids. This leaves too much room for errors.  Bidding in construction is complicated by inflated

Companies are betting big on AI, but very few are seeing the returns they expect. This leads to the inevitable question: why? Is the technology not good enough? The problem often isn’t the technology itself, but a fundamental misunderstanding of what it takes to make it successful. We’re conditioned to see new technology as a

The promise of data democratization, where anyone in an enterprise can get answers from complex data, is compelling. Generative AI and text-to-SQL tools are at the heart of this vision, offering a future where you can simply ask a question and get a data-backed answer. We’ve had clients ask about connecting these tools to vast

MIT’s NANDA initiative just published a study that confirms what those of us in the trenches have been seeing: 95% of enterprise generative AI pilots fail to deliver measurable business value. Despite $30-40 billion in enterprise investment, only 5% of pilots progress beyond early stages to achieve rapid revenue growth. Let me break down what’s

Co-authored by Stew Chisam, Operating Partner, StellarIQ Your best developer just built a production-ready application last weekend. They can’t explain how half of it works. Your product managers are shipping prototypes without involving engineering. And somehow, productivity is up 10X. Welcome to vibe coding: the shift that’s rewriting the rules of software development while you’re

Co-authored by Stew Chisam, Operating Partner, StellarIQ Your company doesn’t have an official AI strategy. The CEO hasn’t set a clear stance on artificial intelligence. In that vacuum, something predictable happens: risk and control functions naturally step in, and their loss-aversion wins. Legal says no to AI tools to avoid compliance violations. Security blocks everything

AI in retail has reached a tipping point. Total Retail’s 2024 survey found that 61% of retailers currently use some form of AI within their businesses, while 76% plan to increase their technology budgets in the next 12 months. AI-powered personalization helps increase customer satisfaction and conversion rates, while optimized AI search boosts conversion rates.

Co-authored by Andy Sweet, VP of Enterprise AI Solutions We are asked to fix broken enterprise AI PoCs literally every day. After countless engagements, we’ve identified clear patterns in why AI initiatives fail and, more importantly, how to fix them. Here’s what we’ve learned. Why Are So Many AI Proofs of Concept Failing? Integrating AI

What is the Bitter Lesson? Your team is six months deep into building a custom AI solution. Next week, a new foundation model makes it obsolete with a simple prompt. Welcome to the Bitter Lesson. The “Bitter Lesson” was articulated by AI researcher Rich Sutton in an influential 2019 essay reflecting on 70 years of

Want to know the real differences between statistical analysis, machine learning, and deep learning? These data analysis approaches are often confused, but each serves distinct purposes in the modern data ecosystem. This article breaks down these concepts in simple terms and helps you understand which approach best fits your business needs. What is Statistical Analysis?